# Real-time Recognition of Yoga Poses using computer Vision for Smart Health Care

arXiv: https://arxiv.org/abs/2201.07594 · HTML: https://mlsanity.com/paper/2201.07594/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 19 Jan 2022.

## Builds on (most cited first, 2 of 2)

- [MediaPipe Hands: On-device Real-time Hand Tracking](https://mlsanity.com/paper/2006.10214/) (2020)
- [Real-Time Hand Tracking Using a Sum of Anisotropic Gaussians Model](https://mlsanity.com/paper/1602.03860/) (2016)

## Abstract (arXiv)

Nowadays, yoga has become a part of life for many people. Exercises and sports technological assistance is implemented in yoga pose identification. In this work, a self-assistance based yoga posture identification technique is developed, which helps users to perform Yoga with the correction feature in Real-time. The work also presents Yoga-hand mudra (hand gestures) identification. The YOGI dataset has been developed which include 10 Yoga postures with around 400-900 images of each pose and also contain 5 mudras for identification of mudras postures. It contains around 500 images of each mudra. The feature has been extracted by making a skeleton on the body for yoga poses and hand for mudra poses. Two different algorithms have been used for creating a skeleton one for yoga poses and the second for hand mudras. Angles of the joints have been extracted as a features for different machine learning and deep learning models. among all the models XGBoost with RandomSearch CV is most accurate and gives 99.2% accuracy. The complete design framework is described in the present paper.